Abstract
Deeply entrenched intuitions and understandings not consistent with commonly accepted statistical facts prevent some students from having success with activities involving statistics. For example, we have talked to many students who, despite knowing the common algorithm for mean, believe that adding zero to a set of data will not change the mean of the set. This notion is consistent with other research findings (Bright and Hoeffner, 1993). Another commonly held belief is that a small sample reliably represents a given population (Garfield and Ahlgren 1988; Shaughnessy 1992). Some people are influenced more by personal anecdotes or advice from individuals than by data gathered from large, diverse samples (Nisbett and Ross 1980). In many situations, conceptions are so deeply ingrained that mere exposure to statistical ideas is not sufficient to overcome them (Mevarech 1983). Simply introducing computational rules will not give students enough experience to overcome their generally inaccurate conceptions and intuitions about statistics. Nor will traditional instruction help students develop a sufficiently deep understanding of key statistical concepts so that they can transfer their knowledge to new problems and situations. Students need to confront their often inaccurate and inconsistent beliefs about statistics through experimentation and prediction (Shaughnessy 1992).
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